Parametric Model Research Articles

Lung cancer survival trends and prognostic factors: A 26-year population-based study in Girona Province, Spain

BackgroundLung cancer (LC) is Europe’s primary cause of cancer-related mortality largely due to its historically low survival rates. The aim of this study was to analyze 26-year survival trends in the province of Girona, Spain, and to identify key prognostic factors. MethodsPopulation-based study of LC cases collected between 1994 and 2019, with follow-up until December 31, 2021. Variables included date of diagnosis, sex, age, histology, and tumor stage (the latter since 2010). Diagnosis dates were categorized into three periods (1994–2002, 2003–2011, and 2012–2019). Multivariate flexible parametric models, incorporating age as a non-linear, time-varying covariate, were used to analyze net survival (NS) and trends. Annual absolute change in survival (AAC_S) was calculated using 3-year NS. ResultsThe analysis of 9,113 LC cases showed a NS improvement between the first and last period (7.1 months (95 %CI: 6.5;7.6) to 8.5 months (95 %CI: 7.9;9.1)). Squamous cell carcinoma (NSC-SCC) showed the greatest improvement with an AAC_S of 0.32 % (95 % CI: 0.21; 0.43), while survival for non-small cell lung cancer not otherwise specified declined (AAC_S of −0.19 % (95 %CI: −0.26; −0.12)). Prognostic analysis of the 3,642 cases (2010–2019) indicated a lower LC death risk for adenocarcinoma and NSC-SCC compared to LC not otherwise specified (HR 0.52 and 0.62, respectively). Increasing tumor stage correlated with higher LC mortality risk (1.8-, 4.0-, and 10.1-fold increase for stage II, III, and IV, respectively, compared to stage I). ConclusionsLC survival has notably improved, particularly for NSC-SCC. Survival is influenced by sex, age, date of diagnosis, tumor histology and especially by stage, underscoring comprehensive data collection’s importance.

Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods

Prediction of Sea Wave parameters is an important issue as it is the main design factor for maritime structures. Previously, researchers have used many parametric and numerical approaches, which may be complex in application, take a long time in preparation and sometimes require a bathymetric survey. Recently, soft computing techniques such as Fuzzy Inference Systems, Genetic Algorithm, Machine Learning, etc. have been used to predict sea wave parameters in many marine areas around the world. The ease of application, high accuracy and low computational time of these techniques make them a very good choice in many engineering applications. This study focuses on prediction of significant wave height (Hs) by applying one of the most advanced Machine Learning techniques known as Support Vector Machine (SVM). SVM models are built on the basis of different Kernel functions (Linear, Sigmoid, Radial Basis Function, and Polynomial) which transform the input data into an n-dimensional space where a hyperplane can be generated to partition the data. The results of SVM models are analyzed, evaluated and then compared with the results of commonly used parametric models (P-M, SPM, and CEM). This study shows that the P-M model has reliable and satisfactory results among all parametric models, as its statistical errors are close to those of SVM models (RBF and Polynomial), while all of them are identical in their correlation factors (0.999). Moreover, the parametric models (SPM and CEM) are more accurate in their results than the SVM models (Linear and Sigmoid). Also, this study confirms that the SVM models (RBF and polynomial) are the most accurate models overall, as they have the best generalization error among all models. Finally, it can be concluded that SVM models (RBF and Polynomial) are a promising technique in the sea wave height prediction and can be used as an economic and accurate alternative solution to other prediction models.

Co-occurrence and correlation of arsenic, cadmium, and lead in processed foods intended for infants and young children: evaluation of two statistical approaches adapted to censored data and their implications for risk management

We evaluated the co-occurrence of arsenic (As), cadmium (Cd), and lead (Pb) in 10 categories of processed foods intended for infants and young children (<2 years old) and adapted methodology to characterise and evaluate the impact of correlations on toxic element (TE) concentrations in these foods. Co-occurrence was assessed by calculating the frequency of samples having concentrations above the limit of detection (LOD) among TE sets (i.e. As-Cd, As-Pb, Cd-Pb, and As-Cd-Pb). Pairwise correlations were evaluated using two statistical approaches adapted to censored data: (i) non-parametric Kendall’s tau and (ii) parametric Bayesian modelling. Nonparametric and parametric correlation analyses showed similar results. We found positive correlations among one or more pairs of the TEs (As, Pb, and Cd) in 9 of the 10 categories of foods intended for infants and young children and a negative correlation for one TE pair for a single food category. Where positive correlations are observed, removing samples with high concentrations of a given TE reduces the mean of the other TEs in that food. We also explored the impact of correlations among TEs on the fraction of the supply below maximum levels. Positive correlations among TEs have the potential to reduce the impact on the supply when setting multiple maximum levels.

UAV Trajectory Tracking Using Proportional-Integral-Derivative-Type-2 Fuzzy Logic Controller with Genetic Algorithm Parameter Tuning.

Unmanned Aerial Vehicle (UAV)-type Quadrotors are highly nonlinear systems that are difficult to control and stabilize outdoors, especially in a windy environment. Many algorithms have been proposed to solve the problem of trajectory tracking using UAVs. However, current control systems face significant hurdles, such as parameter uncertainties, modeling errors, and challenges in windy environments. Sensitivity to parameter variations may lead to performance degradation or instability. Modeling errors arise from simplifications, causing disparities between assumed and actual behavior. Classical controls may lack adaptability to dynamic changes, necessitating adaptive strategies. Limited robustness in handling uncertainties can result in suboptimal performance. Windy environments introduce disturbances, impacting system dynamics and precision. The complexity of wind modeling demands advanced estimation and compensation strategies. Tuning challenges may necessitate frequent adjustments, posing practical limitations. Researchers have explored advanced control paradigms, including robust, adaptive, and predictive control, aiming to enhance system performance amidst uncertainties in a scientifically rigorous manner. Our approach does not require knowledge of UAVs and noise models. Furthermore, the use of the Type-2 controller makes our approach robust in the face of uncertainties. The effectiveness of the proposed approach is clear from the obtained results. In this paper, robust and optimal controllers are proposed, validated, and compared on a quadrotor navigating an outdoor environment. First, a Type-2 Fuzzy Logic Controller (FLC) combined with a PID is compared to a Type-1 FLC and Backstepping controller. Second, a Genetic Algorithm (GA) is proposed to provide the optimal PID-Type-2 FLC tuning. The Backstepping, PID-Type-1 FLC, and PID-Type-2 FLC with GA optimization are validated and evaluated with real scenarios in a windy environment. Deep robustness analysis, including error modeling, parameter uncertainties, and actuator faults, is considered. The obtained results clearly show the robustness of the optimal PID-Type-2 FLC compared to the Backstepping and PID-Type-1 FLC controllers. These results are confirmed by the numerical index of each controller compared to the PID-type-2 FLC, with 12% for the Backstepping controller and 51% for the PID-Type-1 FLC.

Impact of the Affordable Care Act and Medicaid Expansion Among Patients With HIV-Associated Aggressive B-Cell Non-Hodgkin Lymphomas.

To study the influence of the Affordable Care Act (ACA) policy and its Medicaid expansion on insurance status and survival in patients with HIV with aggressive lymphoma. We used the National Cancer Database, a hospital-based national registry, to identify adults age 18-64 years with HIV-associated aggressive B-cell non-Hodgkin lymphomas (HIV-a-B-NHLs), diagnosed during 2007 to 2016. Survival analysis was performed on a subset of patients with HIV-a-B-NHL for whom location data were available who resided in Medicaid expansion-adopted and nonadopted states. Using a quasi-experimental difference-in-difference model, the difference in adjusted 2-year survival rates obtained with a flexible parametric Weibull model was compared for states that adopted the Medicaid expansion of ACA against those that did not adopt the expansion. We identified 8,231 patients with HIV-a-B-NHL and 50,650 non-HIV patients with a-B-NHL. We found that a lower proportion of individuals were uninsured at diagnosis in the expansion states compared with nonexpansion states. We also found that the ACA policy adoption led to a reduction in the proportion of uninsured individuals with HIV-a-B-NHL in expansion states of 34.9%, compared with 15.9% in non-expansion-adopted states. There was a statistically significant improvement in the 2-year survival rate among patients with HIV-a-B-NHL in the expansion compared with nonexpansion states with the adoption of ACA (7.17% v 1.58%, P = .02). Using a novel quasi-experimental model, we found that the ACA policy corresponded with a greater survival improvement in patients with HIV-a-B-NHL within Medicaid expansion-adopted states compared with nonexpansion states. We believe that this evidence should be taken into consideration in future policy making.

A method for evaluating the regulation capacity of thermostatically controlled load group based on state complementary control strategy

Electric heating load (EHL) is a type of thermostatically controlled load whose power consumption mode can be flexibly adjusted, as long as the customers’ thermal comfort is ensured. The key to unlocking the regulatory potential of EHL lies in characterizing and harnessing its post-aggregation regulatory capacity, given its inherent dispersion and heterogeneity. Firstly, the second-order equivalent thermal parameter (ETP) model, grounded in the thermal equilibrium of the room, is employed to analyze the operational characteristics and to construct a model for aggregating load group. Secondly, leveraging the analysis of individual EHL regulation capacity, the temperature state characteristic quantity is introduced as a sorting index. Considering the dynamic properties of baseline power over time, a control strategy is proposed, based on state complementary characteristics among EHLs, for a more comprehensive and dynamic system management. Finally, the key indexes for reporting and implementation phases are identified to evaluate the regulation capacity of load group. The case study evidences that the newly introduced control strategy and evaluation method can provide more stable regulation power support for power grid dispatching, and more deeply harness the regulation capacity to maximize its potential value. In addition, this paper also discusses the influence of related factors such as the number, the rated power, the inside temperature control interval, the inside temperature setting value and the outside temperature on the regulation ability of the EHL group, offering a robust framework for the dispatching center to devise well-informed and rational regulation instructions.

A lumped parameter modelling study of cerebral autoregulation in normal pressure hydrocephalus suggests the brain chooses to be ischemic

Normal pressure hydrocephalus (NPH) is associated with a reduction in cerebral blood flow and an ischemic metabolic state. Ischemia should exhaust the available autoregulation in an attempt to correct the metabolic imbalance. There is evidence of some retained autoregulation reserve in NPH. The aim of this study is to model the cerebral autoregulation in NPH to discover a solution to this apparent paradox. A lumped parameter model was developed utilizing the known limits of autoregulation in man. The model was tested by predicting the cerebral blood volume changes which would be brought about by changes in resistance. NPH and the post shunt state were then modeled using the known constraints provided from the literature. The model successfully predicted the cerebral blood volume changes brought about by altering the cerebral perfusion pressure to the limit of autoregulation. The model suggests that NPH is associated with a balanced increase in resistance within the arterial and venous outflow segments. The arterial resistance decreased after modelling shunt insertion. The model suggests that the cerebral blood flow is actively limited in NPH by arteriolar constriction. This may occur to minimize the rise in ICP by reducing the apparent CSF formation rate.

Evaluation of GOCE/GRACE and combined global geopotential models using GNSS/levelling data over Nigeria

AbstractGlobal Geopotential Models (GGMs) provide valuable information about Earth’s gravity field functionals, such as geoid heights and gravity anomalies. However, ground-based datasets are required to validate these GGMs at the regional and local scales. In this study, we validated the accuracy of GGMs by comparing them with ground-based Global Navigation Satellite System (GNSS)/levelling data for the first time in Nigeria. We employed two validation scenarios: with and without considering spectral consistency using the spectral enhancement method (SEM) to incorporate high and very high frequencies of the gravity field spectrum from the combined global gravity field model (XGM2019e_2159) and the residual terrain model (RTM) derived from the Shuttle Radar Topography Mission (SRTM) data, respectively. The results of this evaluation confirmed that the application of SEM improved the assessment of the GGM solutions in an unbiased manner. Integrating XGM2019e_2159 and SRTM data to constrain the high-frequency component of geoid heights in Gravity Field and Steady-State Ocean Circulation Explorer (GOCE)-based GGMs led to an improvement of approximately 10% in reducing the standard deviation (STD) relative to when SEM was not applied. GO_CONS_GCF_2_TIM_R6 at spherical harmonics (SH) of up to degree and order 260 demonstrated the lowest STD when compared to GO_CONS_GCF_2_DIR_R6 and GO_CONS_GCF_2_SPW_R5, with a reduction from 0.380 m without SEM application to 0.342 m with SEM implementation. In addition, four transformation models, namely, linear, four-parameter, five-parameter, and seven-parameter models, were evaluated. The objective is to mitigate the reference system offsets between the GNSS/levelling data and the GGMs and to identify the particular parametric model with the smallest STD across all GGMs. This effort reduced the GGMs misfits to GNSS/levelling to 0.30 m, representing a 15.3% decrease in STD. Notably, the XGM2019e_2159 model provides this improvement.

Advanced Predictive Structural Health Monitoring in High-Rise Buildings Using Recurrent Neural Networks

This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas.

Developmental patterns of inhibition and fronto-basal-ganglia white matter organisation in healthy children and children with attention-deficit/hyperactivity disorder.

There is robust evidence implicating inhibitory deficits as a fundamental behavioural phenotype in children with attention-deficit/hyperactivity disorder (ADHD). However, prior studies have not directly investigated the role in which white matter properties within the fronto-basal-ganglia circuit may play in the development of inhibitory control deficits in this group. Combining recent advancements in brain-behavioural modelling, we mapped the development of stop-signal task (SST) performance and fronto-basal-ganglia maturation in a longitudinal sample of children aged 9-14 with and without ADHD. In a large sample of 135 ADHD and 138 non-ADHD children, we found that the ADHD group had poorer inhibitory control (i.e., longer stop-signal reaction times) across age compared to non-ADHD controls. When applying the novel parametric race model, this group effect was driven by higher within-subject variability (sigma) and higher number of extreme responses (tau) on stop trials. The ADHD group also displayed higher within-subject variability on correct responses to go stimuli. Moreover, we observed the ADHD group committing more task-based failures such as responding on stop trials (trigger failures) and omissions on go trials (go failures) compared to non-ADHD controls, suggesting the contribution of attentional lapses to poorer response inhibition performance. In contrast, longitudinal modelling of fixel-based analysis measures revealed no significant group differences in the maturation of fronto-basal-ganglia fibre cross-section in a subsample (74 ADHD and 73 non-ADHD children). Finally, brain-behavioural models revealed that age-related changes in fronto-basal-ganglia morphology (fibre cross-section) were significantly associated with reductions in the variability of the correct go-trial responses (sigma.true) and skew of the stop-trial distribution (tauS). However, this effect did not differ between ADHD and typically developing children. Overall, our findings support the growing consensus suggesting that attentional deficits subserve ADHD-related inhibitory dysfunction. Furthermore, we show novel evidence suggesting that while children with ADHD are consistently performing worse on the SST than their non-affected peers, they appear to have comparable rates of neurocognitive maturation across this period.

Reliability analysis based on doubly-truncated and interval-censored data

Field data provide important information on product reliability. Interval sampling is widely used for collection of field data, which typically report incident cases during a certain time period. Such sampling scheme induces doubly truncated (DT) data if the exact failure time is known. In many situations, the exact failure date is known only to fall within an interval, leading to doubly truncated and interval censored (DTIC) data. This article considers analysis of DTIC data under parametric failure time models. We consider a conditional likelihood approach and propose interval estimation for parameters and the cumulative distribution functions. Simulation studies show that the proposed method performs well for finite sample size.

A viscoelastic harvester-absorber for energy generation and vibration control

Abstract A vibration energy harvester harnesses the oscillatory movement and&amp;#xD;converts it into electrical energy. On the other hand, a (dynamic) vibration absorber&amp;#xD;dissipates the mechanical energy of a host structure (primary system). The proposal for&amp;#xD;a harvester-absorber device (HAD), comprinsing a piezoelectric element (conventional&amp;#xD;harvester) bonded with a viscoelastic layer and a steel constrained layer, is based on the&amp;#xD;dual purpose of generating energy and at the same time reducing the vibratory response&amp;#xD;of the primary system. Introducing a viscoelastic dissipative layer in a conventional&amp;#xD;harvester, also offers the advantage of simultaneously reducing wear on the piezoelectric&amp;#xD;element, thereby extending its lifespan and preventing fatigue-related breakage. The&amp;#xD;paper presents a model of a harvester-absorber built from a bimorph piezoelectric&amp;#xD;beam, a layer of viscoelastic material (E-A-R C1002-01PSA) and a beam of steel, which&amp;#xD;acts as a constrained layer. A lumped parameter model is proposed for the theoretical&amp;#xD;description of the HAD which is successfully used to experimentally determine its&amp;#xD;parameters. After that, and using the equivalent stiffness concept, the HAD was&amp;#xD;coupled to a free-free beam with two masses at its ends to study its perfomance over a&amp;#xD;real host structure. The inertance of the composite system and voltage/force frequency&amp;#xD;response function of the HAD were obtained and compared with experimental results.&amp;#xD;The accuracy of the results justifies and validate the model which has the advantage&amp;#xD;of simplicity and can contribute to reducing wear and increasing the lifespan of brittle&amp;#xD;piezoelectric structures.

New Buckling Curve for a Compressed Member with Cold-Formed Channel Cross-Section

The verification of a column made from a lipped cold-formed channel section, subjected to pure axial compression relative to the gross cross-section, often results in a combined verification of bending and compression due to the appearance of a shift of the centroid of its effective cross-section. Following Eurocode 3 rules, this requires the determination of two distinct effective cross-sections and various interaction factors. This paper, based on an analytic approach, offers a modification to the actual buckling curve, based on Ayrton–Perry formulation, to include the second-order effects raised by the eventual shift of the effective centroid due to local buckling of the compressed web plate. This eliminates the need to use an interaction formula. The modified buckling curve is verified based on a GMNIA analysis performed on a numerical parametric model, which was previously validated by laboratory tests. In addition, the results are compared with strength results provided by appropriate Eurocode 3 formulas and AISI Direct Strength Method for global-local interaction and with classic experimental results.

Machine learning-based personalized composite score dissects risk and protective factors for cognitive and motor function in older participants.

With age, sensory, cognitive, and motor abilities decline, and the risk for neurodegenerative disorders increases. These impairments influence the quality of life and increase the need for care, thus putting a high burden on society, the economy, and the healthcare system. Therefore, it is important to identify factors that influence healthy aging, particularly ones that are potentially modifiable through lifestyle choices. However, large-scale studies investigating the influence of multi-modal factors on a global description of healthy aging measured by multiple clinical assessments are sparse. We propose a machine learning model that simultaneously predicts multiple cognitive and motor outcome measurements on a personalized level recorded from one learned composite score. This personalized composite score is derived from a large set of multi-modal components from the TREND cohort, including genetic, biofluid, clinical, demographic, and lifestyle factors. We found that a model based on a single composite score was able to predict cognitive and motor abilities almost as well as a classical flexible regression model specifically trained for each single clinical score. In contrast to the flexible regression model, our composite score model is able to identify factors that globally influence cognitive and motoric abilities as measured by multiple clinical scores. The model identified several risk and protective factors for healthy aging and recovered physical exercise as a major, modifiable, protective factor. We conclude that our low parametric modeling approach successfully recovered known risk and protective factors of healthy aging on a personalized level while providing an interpretable composite score. We suggest validating this modeling approach in other cohorts.

Feed shaft thermal expansion error modeling based on principal component regression

In order to further improve the prediction accuracy of the thermal error model of the feed shaft of the gear grinding machine, this paper proposes a feed shaft thermal expansion error modeling method based on principal component regression. The feed shaft positioning error is decoupled by linear fitting to obtain the thermal expansion slope parameter, the position correlation of the feed shaft thermal expansion error is eliminated, and a regression model of the thermal expansion slope parameter and the temperature of all measuring points is established based on the principal component regression algorithm. Different from the traditional method, the principal component regression model does not require additional screening of temperature sensitive points, and the root mean square error mean and standard deviation of the group experimental prediction results can reach 2.0 μm/m、0.9μm/m, which has higher accuracy and stability than conventional methods.

Understanding bi-modal seas, shingle beach response and flood risk at Hayling island, UK

Beach management through nourishment and annual recycling has been applied for over 37 years to manage flood and coastal erosion risk to 1700 properties at Eastoke, Hayling Island, UK. This form of natural flood risk management has proved successful in avoiding the annual flooding that blighted this area previously. However there have been unexpected storm events which have caused beach erosion and localised flooding. Using long-term nearshore wave datasets and state-of-the-art statistical methods, new multivariate extreme wave conditions were derived. The conditions were applied to simulate beach profile response, estimated bimodal overtopping rates and resulting flood inundation, then validated against recent storm events. The Eastoke beach was found to meet its original design criteria of a 0.5% AEP standard of protection for unimodal wave conditions. However, it experiences greater roll back erosion, wave overwash and therefore flood inundation under certain bimodal wave conditions, causing uncertainty around the future standard of protection.It remains unclear if the unexpected beach response at Eastoke resulted from increased storminess due to climate change, additional loading by mixed sea/swell events, increasing bimodal events, or the assumptions within the original bivariate analysis, leading to incorrect AEP attribution.Communicating these issues to practitioners and a non-specialist audience is challenging as we tend to oversimplify, despite every beach and storm being unique. Better understanding of mixed beaches both in situ and through parametric and numerical models would reduce uncertainty, to ensure communities are resilient to climate change.

COMPARATIVE ANALYSIS OF TIME SERIES FORECASTING MODELS USING ARIMA AND NEURAL NETWORK AUTOREGRESSION METHODS

Gold price fluctuations have a significant impact because gold is a haven asset. When financial markets are volatile, investors tend to turn to safer instruments such as gold, so gold price forecasting becomes important in economic uncertainty. The novelty of this research is the comparative analysis of time series forecasting models using ARIMA and the NNAR methods to predict gold price movements specifically applied to gold price data with non-stationary and non-linear characteristics. The aim is to identify the strengths and limitations of ARIMA and NNAR on such data. ARIMA can only be applied to time series data that are already stationary or have been converted to stationary form through differentiation. However, ARIMA may struggle to capture complex non-linear patterns in non-stationary data. Instead, NNAR can handle non-stationary data more effectively by modeling the complex non-linear relationships between input and output variables. In the NNAR model, the lag values of the time series are used as input variables for the neural network. The dataset used is the closing price of gold with 1449 periods from January 2, 2018, to October 5, 2023. The augmented Dickey-Fuller test dataset obtained a p-value = 0.6746, meaning the data is not stationary. The ARIMA(1, 1, 1) model was selected as the gold price forecasting model and outperformed other candidate ARIMA models based on parameter identification and model diagnosis tests. Model performance is evaluated based on the RMSE and MAE values. In this study, the ARIMA(1, 1, 1) model obtained RMSE = 16.20431 and MAE = 11.13958. The NNAR(1, 10) model produces RMSE = 16.10002 and MAE = 11.09360. Based on the RMSE and MAE values, the NNAR(1, 10) model produces better accuracy than the ARIMA(1, 1, 1) model.

Differentiation between high-grade gliomas and solitary brain metastases based on multidiffusion MRI model quantitative analysis.

Differentiating high-grade gliomas (HGGs) from solitary brain metastases (SBMs) using conventional magnetic resonance imaging (MRI) remains challenging due to their similar imaging features. This study aimed to evaluate the diagnostic performance of advanced diffusion models, such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator magnetic resonance imaging (MAP-MRI), incomparison to traditional techniques like diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI) for distinguishing HGGs from SBMs. In total, 17 patients with HGGs and 26 patients with SBMs were prospectively recruited based on the established inclusion and exclusion criteria. Structural MRI sequences and diffusion spectrum imaging (DSI) were utilized to assess quantitative parameter models, including NODDI, MAP-MRI, DWI, DTI, and DKI. Quantitative parameters were measured for both the tumor parenchymal area and the peritumoral edema area. The quantitative parameters of the two patient groups were compared using either the independent Student's t-test or the Mann-Whitney U test. The effectiveness of each model was evaluated using receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Finally, the DeLong test was employed to compare the diagnostic performance of each model through pairwise comparisons of ROC curves. Isotropic volume fraction (Viso) based on NODDI; mean squared displacement (MSD) and the return to plane probabilities (RTPP) based on MAP-MRI; radial diffusivity (RDk) and mean diffusivity (MDk) based on DKI; and axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) based on DTI of the peritumoral edema tumor were significantly different between HGGs and SBMs (p < 0.05). The optimal single discriminant parameters for each model are NODDI_Viso, MAP-MRI_MSD, DKI_MDk, and DTI_AD. Among these, the AUC of Viso (0.809) exceeds that of MSD (0.733), MDk (0.718), and AD (0.779). The combined model, which incorporates DTI_AD, DKI_RD, and NODDI_Viso, demonstrated superior diagnostic performance (0.897). Advanced diffusion MRI quantitative parameters derived from NODDI, such as Viso, have the potential to enhance the differentiation between HGGs and SBMs. The integrated utilization of these models is anticipated to enhance diagnostic accuracy and refine MRI protocols for brain tumor assessment.

Research on Performance of Interior Permanent Magnet Synchronous Motor with Fractional Slot Concentrated Winding for Electric Vehicles Applications

The fractional-slot, concentrated-winding, interior permanent magnet synchronous motor (FSCW IPMSM) has advantages, such as reducing motor copper consumption, improving flux-weakening capability, and motor fault tolerance, and has certain development potential in application fields such as electric vehicles. However, fractional-slot concentrated-winding motors often contain rich harmonic components due to their winding characteristics, leading to increased motor losses and back electromotive force harmonics, thereby affecting the efficiency and constant power speed regulation range of the motor. Based on this, this article first uses the winding function method to explore the inductance and saliency ratio of the interior permanent magnet synchronous motor with different slot pole combinations in the fractional-slot concentrated- winding of electric vehicles. Secondly, this article will establish a 2D finite element parameterized model to analyze and compare the performance of fractional-slot concentrated-winding motors with different slot pole combinations, including air gap magnetic density, back electromotive force distortion rate, overload multiple, and torque. The structural parameters of the motor were optimized with the objective of minimizing the torque ripple under the constraint of minimizing the average torque reduction. The motor slot width, permanent magnet angle, and permanent magnet pole arc angle were analyzed and optimized. The simulation results showed that 12 slots and 8 poles were the optimal design schemes, providing a theoretical basis for the selection of slot pole coordination in the fractional-slot concentrated-winding interior permanent magnet synchronous motor for electric vehicles.

Identifying risk factors for recurrent multidrug resistant tuberculosis based on patient’s record data from 2016 to 2021: retrospective study

Globally, the prevalence of multidrug-resistant tuberculosis (MDR-TB) has been increasing recently. This is a major public health concern, as MDR-TB is more difficult to treat and has poorer outcomes compared to drug-sensitive tuberculosis. The main objective of the study was to identify risk factors for recurrent multidrug-resistant tuberculosis, at Alert Specialized Hospital, Addis Ababa, by using different parametric shared frailty models. From January 2016 to December 2021, a retrospective study was conducted on MDR-TB patients at Alert Specialized Hospital in Addis Ababa. The data for the study were collected from the medical records of MDR-TB patients at the hospital during this time period. Gamma and inverse-Gaussian shared frailty models were used to analyze the dataset, with the exponential, Weibull, and lognormal distributions included as baseline hazard functions. The data were analyzed using R statistical software. The median recurrence time of the patients was 12 months, and 149 (34.3%) had recurrences. The clustering effect was statistically significant for multiple drug-resistant tuberculosis patients’ recurrence. According to the Weibull-Inverse-Gaussian model, factors that reduced time to MDR-TB recurrence included lower weight (ɸ = 0.944), smoking (ɸ = 0.045), alcohol use (ɸ = 0.631), hemoptysis (ɸ = 0.041), pneumonia (ɸ = 0.564), previous anti-TB treatment (ɸ = 0.106), rural residence (ɸ = 0.163), and chronic diseases like diabetes (ɸ = 0.442) were associated with faster recurrence. While, higher education (ɸ = 3.525) and age (ɸ = 1.021) extended time to recurrence. For weight increment, smokers and alcohol users, clinical complications of hemoptysis and pneumonia, patients with pulmonary disease who had a history of previous anti-TB treatment, and being rural residents are prognostic factors. There was a significant clustering effect at the Alert Specialized Hospital in Addis Ababa, Ethiopia. The Weibull-Inverse Gaussian Shared Frailty Model was chosen as the best model for predicting the time to recurrence of MDR-TB.